Many of the major advances in A.I. have been made due to reverse engineering biological brains, including Deep Neural Networks (DNNs) and Reinforcement Learning (RL) ([1]}). Research into A.I. has been given a boost in recent decades due to success in reverse engineering neural connections and functions of the mammalian cortex, and in reverse, machine learning approaches have helped computational neuroscientists in learning spike patterns from neural data. So-called “deep learning” frameworks ([2]}) adopt the feed-forward, layered aspects of the connections in the brain, while some (convolutional networks) go further to mimic the convolutional connections that are present in many pathways. These approaches have demonstrated human level accuracy in some tasks, such as face detection from static images ([3]}). However, because they rely on non-biological learning mechanisms, it is difficult to use these networks to uncover biological mechanisms for neural information encoding. In addition, these networks still struggle to classify temporal data; this will be a critical next step for A.I. systems of the future. In the neuroscience community, it is a current debate as to whether the information is encoded in the firing rate of the neuron, or if the information is encoded in the precise timing of incoming spikes ([4]}, [5]}, [6]}). It has been shown that unsupervised learning rules, such as STDP (section 1.4), are capable of encoding this spike-timing information into the synaptic weights ([7]}, [8]}). It has also been argued that through these learning rules, the neurons compete with one another to form groups in a selection process ([9]}). Here we investigate whether biologically-inspired neural plasticity rules combine with precise timing of incoming firing rates to encode information about novel stimuli in the form of a biologically-inspired camera in a multi-layered network.
